Spatial Data and Mapping in R
Jon Minton
2022-05-23
Aim
- Theory
- Geometric perspective
- Data structure perspective
- Packages
sf- base plotting functions
- tmap
- ggplot2
- Examples
- Basic choropleth
- Basemap + points
- Interactive lookup?
Theory - geometries
- High level distinction between types of spatial data
- raster files: rectangular bitmaps of values
- look different on different resolution displays
- vector files: mathematical descriptions of
relationships between collections of attributes/features
- key example of vector graphics: fonts
- raster graphics may be used as a base map; then vector-based
features added on top
- Geometric perspective
- points, lines, polylines, polygons, collections
- attributes to above entities
- Attributes can include names, identifiers etc, which allow linking
to other features in databases
- e.g. datazones, intermediate geographies, healthboards etc.
- also need to consider projections
theory - data structures
- Data structure perspective
- basic data types in R include integers, real numbers, complex
numbers, characters etc
- and include lists
- lists can take unnamed and named elements of different types and
lengths
- this includes other lists, allowing deeply nested, ‘ragged’
collections of elements within a list
- dataframes
- data frames are non-ragged lists, where elements within each list
object are of the same type
- this includes columns that can contain only other lists (a
list column)
- also have an additional
data.frame attribute attached
to their class
- tibbles have further attributes added (
tbl_df), in
addition to data.frame
- this changes how some other functions work with the objects, by
evoking class-specific methods.
- try
methods("print") or methods("plot")
for examples
- tidyverse
- encourages working with tibbles (rectangular data) and using
verb-functions to interact with them
- but spatial entities are not inherently rectangular
- a rougher geometry may require more coordinates to define it than a
smoother geography
- an administrative geography including part of the mainland and an
island will require more collections of polygons than one that only
includes the mainland
- The older
sp (spatial) package was
developed before the tidyverse, and did not work easily with tidyverse
paradigms/verbs etc based around rectangular data.
The sf package
sf: ‘simple features’- Introduction
- simple geography types include:
POINT, LINESTRING,
POLYGONMULTIPOINT, MULTILINESTRING,
MULTIPOLYGONGEOMETRYCOLLECTION (somewhat like a list?)- ten additional (rarer) geography types
- how are these stored?
- an
sf class data.frame contains
- an
sfc class list-column “with the geometries for each
feature (record), which is composed of”
sfg: “the feature geography of an indivdual simple
feature”
Example with sf
Let’s try the example code in the above intro
pacman::p_load(tidyverse, sf)
nc <- st_read(system.file("shape/nc.shp", package="sf"))
## Reading layer `nc' from data source
## `C:\Users\Jon Minton\AppData\Local\R\win-library\4.2\sf\shape\nc.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 100 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## Geodetic CRS: NAD27
## [1] "sf" "data.frame"
## Simple feature collection with 100 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## Geodetic CRS: NAD27
## First 10 features:
## AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74 SID74
## 1 0.114 1.442 1825 1825 Ashe 37009 37009 5 1091 1
## 2 0.061 1.231 1827 1827 Alleghany 37005 37005 3 487 0
## 3 0.143 1.630 1828 1828 Surry 37171 37171 86 3188 5
## 4 0.070 2.968 1831 1831 Currituck 37053 37053 27 508 1
## 5 0.153 2.206 1832 1832 Northampton 37131 37131 66 1421 9
## 6 0.097 1.670 1833 1833 Hertford 37091 37091 46 1452 7
## 7 0.062 1.547 1834 1834 Camden 37029 37029 15 286 0
## 8 0.091 1.284 1835 1835 Gates 37073 37073 37 420 0
## 9 0.118 1.421 1836 1836 Warren 37185 37185 93 968 4
## 10 0.124 1.428 1837 1837 Stokes 37169 37169 85 1612 1
## NWBIR74 BIR79 SID79 NWBIR79 geometry
## 1 10 1364 0 19 MULTIPOLYGON (((-81.47276 3...
## 2 10 542 3 12 MULTIPOLYGON (((-81.23989 3...
## 3 208 3616 6 260 MULTIPOLYGON (((-80.45634 3...
## 4 123 830 2 145 MULTIPOLYGON (((-76.00897 3...
## 5 1066 1606 3 1197 MULTIPOLYGON (((-77.21767 3...
## 6 954 1838 5 1237 MULTIPOLYGON (((-76.74506 3...
## 7 115 350 2 139 MULTIPOLYGON (((-76.00897 3...
## 8 254 594 2 371 MULTIPOLYGON (((-76.56251 3...
## 9 748 1190 2 844 MULTIPOLYGON (((-78.30876 3...
## 10 160 2038 5 176 MULTIPOLYGON (((-80.02567 3...
Note the geometry column
example with sf - continued
## Simple feature collection with 100 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## Geodetic CRS: NAD27
## First 3 features:
## BIR74 SID74 NWBIR74 BIR79 SID79 NWBIR79 geometry
## 1 1091 1 10 1364 0 19 MULTIPOLYGON (((-81.47276 3...
## 2 487 0 10 542 3 12 MULTIPOLYGON (((-81.23989 3...
## 3 3188 5 208 3616 6 260 MULTIPOLYGON (((-80.45634 3...
example with sf - continued
Pull out the geometry column
(nc_geom <- st_geometry(nc))
## Geometry set for 100 features
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## Geodetic CRS: NAD27
## First 5 geometries:
## MULTIPOLYGON (((-81.47276 36.23436, -81.54084 3...
## MULTIPOLYGON (((-81.23989 36.36536, -81.24069 3...
## MULTIPOLYGON (((-80.45634 36.24256, -80.47639 3...
## MULTIPOLYGON (((-76.00897 36.3196, -76.01735 36...
## MULTIPOLYGON (((-77.21767 36.24098, -77.23461 3...
## MULTIPOLYGON (((-81.47276 36.23436, -81.54084 36.27251, -81.56198 36.27359, -81.63306 36.34069, -81.74107 36.39178, -81.69828 36.47178, -81.7028 36.51934, -81.67 36.58965, -81.3453 36.57286, -81.34754 36.53791, -81.32478 36.51368, -81.31332 36.4807, -81.26624 36.43721, -81.26284 36.40504, -81.24069 36.37942, -81.23989 36.36536, -81.26424 36.35241, -81.32899 36.3635, -81.36137 36.35316, -81.36569 36.33905, -81.35413 36.29972, -81.36745 36.2787, -81.40639 36.28505, -81.41233 36.26729, -81.43104 36.26072, -81.45289 36.23959, -81.47276 36.23436)))
plotting
example with sf - continued
## Warning: plotting the first 10 out of 14 attributes; use max.plot = 14 to plot
## all
# We can use tidyverse functions with the nc object (as it's a data.frame)
nc %>%
arrange(desc(AREA))
## Simple feature collection with 100 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## Geodetic CRS: NAD27
## First 10 features:
## AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74 SID74
## 1 0.241 2.214 2083 2083 Sampson 37163 37163 82 3025 4
## 2 0.240 2.004 2150 2150 Robeson 37155 37155 78 7889 31
## 3 0.240 2.365 2232 2232 Columbus 37047 37047 24 3350 15
## 4 0.225 2.107 2162 2162 Bladen 37017 37017 9 1782 8
## 5 0.219 2.130 1938 1938 Wake 37183 37183 92 14484 16
## 6 0.214 2.152 2185 2185 Pender 37141 37141 71 1228 4
## 7 0.212 2.024 2241 2241 Brunswick 37019 37019 10 2181 5
## 8 0.207 1.851 1989 1989 Johnston 37101 37101 51 3999 6
## 9 0.204 1.871 2100 2100 Duplin 37061 37061 31 2483 4
## 10 0.203 3.197 2004 2004 Beaufort 37013 37013 7 2692 7
## NWBIR74 BIR79 SID79 NWBIR79 geometry
## 1 1396 3447 4 1524 MULTIPOLYGON (((-78.11377 3...
## 2 5904 9087 26 6899 MULTIPOLYGON (((-78.86451 3...
## 3 1431 4144 17 1832 MULTIPOLYGON (((-78.65572 3...
## 4 818 2052 5 1023 MULTIPOLYGON (((-78.2615 34...
## 5 4397 20857 31 6221 MULTIPOLYGON (((-78.92107 3...
## 6 580 1602 3 763 MULTIPOLYGON (((-78.02592 3...
## 7 659 2655 6 841 MULTIPOLYGON (((-78.65572 3...
## 8 1165 4780 13 1349 MULTIPOLYGON (((-78.53874 3...
## 9 1061 2777 7 1227 MULTIPOLYGON (((-77.68983 3...
## 10 1131 2909 4 1163 MULTIPOLYGON (((-77.10377 3...
Plotting with ggplot
ggplot(data = nc) +
geom_sf()
We can use the fill attribute. For example, let’s make the fill
dependent on the ratio of perimeter to area, so wigglier/more connected
places are hotter colours r
nc %>%
mutate(
wiggliness = PERIMETER / AREA
) %>%
ggplot() +
geom_sf(aes(fill = wiggliness), color = NA)
Plotting with tmap
tmap is quite similar to ggplot2 in operation, but just
focused on maps, and having defaults that work well with spatial
data
pacman::p_load(tmap)
qtm(nc) # qtm : 'quick tmap', analogous to ggplot2::qplot()
And to fill polygons based on a value
tm_shape(nc) + tm_fill("AREA") # note area in quotes, also use of tm_shape first
nc %>%
mutate(
wiggliness = PERIMETER / AREA
) %>%
tm_shape() +
tm_fill("wiggliness")
Plotting with interactive tmap
tmap has an option to create interactive version of the
above (static) maps, using the leaflet package.
## tmap mode set to interactive viewing
nc %>%
mutate(
wiggliness = PERIMETER / AREA
) %>%
tm_shape() +
tm_fill("wiggliness")
Note the addition of a standard base map layer.
to switch back between modes
ttm() # toggles between interactive and static
## tmap mode set to plotting
tmap_mode("plot") # switches to static (even if already on static mode)
## tmap mode set to plotting
tmap basemaps
When in view mode (using leaflet), different basemaps
can be selected by specifying a valid selection from
leaflet::providers). Here’s the default example:
current.mode <- tmap_mode("view")
## tmap mode set to interactive viewing
data(World, metro)
tm_basemap(leaflet::providers$Stamen.Watercolor) +
# tm_basemap(leaflet::providers$CartoDB) +
tm_shape(metro, bbox = "India") + tm_dots(col = "red", group = "Metropolitan areas") +
tm_tiles(paste0("http://services.arcgisonline.com/arcgis/rest/services/Canvas/",
"World_Light_Gray_Reference/MapServer/tile/{z}/{y}/{x}"), group = "Labels")
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
Both the basemap and tiles are rasters (bitmap image files). The
difference is that tiles go on top and the basemap is at the bottom.
(maps being composed of successive layers, much like ggplots)
Application to Scottish data
defib_points <- st_read("Defibrillators/Defibrillators.shp")
## Reading layer `Defibrillators' from data source
## `C:\Users\Jon Minton\repos\tardy_tuesday_spatial\Defibrillators\Defibrillators.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 124 features and 5 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 266833 ymin: 696948 xmax: 326107 ymax: 766039
## Projected CRS: OSGB 1936 / British National Grid
Scottish example continued
Maybe we can use leaflet with the view mode to see where
these are
current.mode <- tmap_mode("view")
## tmap mode set to interactive viewing
tm_basemap(leaflet::providers$Stamen.Watercolor) +
# tm_basemap(leaflet::providers$CartoDB) +
tm_shape(defib_points) + tm_dots()
Scottish example continued
- The map maybe isn’t detailed enough (though looks pretty)
- We can look at other leaflet basemaps here
- I’m going to try
OpenStreetMap.Mapnik
current.mode <- tmap_mode("view")
## tmap mode set to interactive viewing
tm_basemap(leaflet::providers$OpenStreetMap.Mapnik) +
# tm_basemap(leaflet::providers$CartoDB) +
tm_shape(defib_points) + tm_dots()
With SIMD
We can also attempt to combine layers of different types
- Defibrillators are points
- SIMD are polygons
There are neater ways of doing this, but I’ve downloaded the SIMD
2020 shapefiles here
simd_shapes <- st_read("big_data/SG_SIMD_2020/SG_SIMD_2020.shp")
## Reading layer `SG_SIMD_2020' from data source
## `C:\Users\Jon Minton\repos\tardy_tuesday_spatial\big_data\SG_SIMD_2020\SG_SIMD_2020.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 6976 features and 51 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 5513 ymin: 530252.8 xmax: 470323 ymax: 1220302
## Projected CRS: OSGB 1936 / British National Grid
## Simple feature collection with 6976 features and 51 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 5513 ymin: 530252.8 xmax: 470323 ymax: 1220302
## Projected CRS: OSGB 1936 / British National Grid
## First 10 features:
## DataZone DZName LAName SAPE2017
## 1 S01006506 Culter - 01 Aberdeen City 894
## 2 S01006507 Culter - 02 Aberdeen City 793
## 3 S01006508 Culter - 03 Aberdeen City 624
## 4 S01006509 Culter - 04 Aberdeen City 537
## 5 S01006510 Culter - 05 Aberdeen City 663
## 6 S01006511 Culter - 06 Aberdeen City 759
## 7 S01006512 Culter - 07 Aberdeen City 539
## 8 S01006513 Cults, Bieldside and Milltimber West - 01 Aberdeen City 788
## 9 S01006514 Cults, Bieldside and Milltimber West - 02 Aberdeen City 1123
## 10 S01006515 Cults, Bieldside and Milltimber West - 03 Aberdeen City 816
## WAPE2017 Rankv2 Quintilev2 Decilev2 Vigintilv2 Percentv2 IncRate IncNumDep
## 1 580 4691 4 7 14 68 8% 71
## 2 470 4862 4 7 14 70 5% 43
## 3 461 5686 5 9 17 82 6% 40
## 4 307 4332 4 7 13 63 10% 52
## 5 415 3913 3 6 12 57 10% 68
## 6 453 6253 5 9 18 90 4% 30
## 7 345 5692 5 9 17 82 2% 13
## 8 406 6177 5 9 18 89 2% 14
## 9 709 6715 5 10 20 97 2% 17
## 10 529 6363 5 10 19 92 1% 5
## IncRankv2 EmpRate EmpNumDep EmpRank HlthCIF HlthAlcSR HlthDrugSR HlthSMR
## 1 3936 8% 49 3220 65 29 30 70
## 2 4829 5% 25 4481 45 130 126 81
## 3 4460 4% 19 5110 45 71 18 41
## 4 3481 8% 26 3229 80 80 28 103
## 5 3344 8% 32 3448 95 89 44 139
## 6 5469 4% 17 5346 50 55 0 54
## 7 6264 2% 8 6206 40 29 0 41
## 8 6572 3% 13 5695 40 28 0 138
## 9 6704 2% 12 6658 25 18 0 107
## 10 6955 1% 7 6803 25 23 0 34
## HlthDprsPc HlthLBWTPc HlthEmergS HlthRank EduAttend EduAttain EduNoQuals
## 1 13% 0% 74 5174 85% 5.88 53
## 2 14% 0% 86 5051 85% 5.96 96
## 3 13% 4% 69 5942 90% 5.75 39
## 4 16% 5% 88 3871 94% 6.20 80
## 5 22% 5% 89 3049 80% 5.87 77
## 6 12% 13% 73 5783 90% 5.79 54
## 7 11% 6% 55 6586 93% 5.86 27
## 8 21% 0% 72 5516 94% 6.10 40
## 9 12% 8% 59 6733 94% 6.50 19
## 10 10% 0% 58 6846 96% 6.00 15
## EduPartici EduUniver EduRank GAccPetrol GAccDTGP GAccDTPost GAccDTPsch
## 1 0% 30% 5887 2.54 3.07 1.62 2.62
## 2 2% 12% 4384 3.92 4.31 2.56 3.65
## 3 1% 19% 5915 3.32 3.78 1.44 3.25
## 4 0% 25% 6401 2.62 2.78 2.62 1.94
## 5 6% 16% 4092 2.12 2.36 2.41 1.85
## 6 2% 13% 5410 1.52 1.77 3.23 2.52
## 7 0% 15% 6506 5.37 5.78 3.91 5.37
## 8 3% 28% 6531 3.57 4.32 5.52 1.95
## 9 1% 22% 6847 2.72 3.52 4.69 2.89
## 10 0% 20% 6838 4.44 5.18 6.34 3.06
## GAccDTSsch GAccDTRet GAccPTGP GAccPTPost GAccPTRet GAccBrdbnd GAccRank
## 1 9.93 1.54 8.86 5.86 6.02 11% 4724
## 2 11.04 2.85 9.98 7.52 7.93 1% 2148
## 3 10.62 2.06 8.62 4.32 5.77 1% 4200
## 4 10.04 2.16 7.94 8.43 8.33 11% 3982
## 5 9.65 1.78 5.57 6.97 6.63 0% 5588
## 6 8.61 2.59 4.93 7.67 7.34 5% 4974
## 7 12.22 4.28 19.01 16.26 16.96 54% 547
## 8 6.26 4.91 11.27 10.98 11.10 3% 1490
## 9 7.04 4.06 9.74 10.00 9.73 14% 1858
## 10 6.88 5.78 17.18 17.09 17.03 22% 659
## CrimeCount CrimeRate CrimeRank HouseNumOC HouseNumNC HouseOCrat HouseNCrat
## 1 11 125 4664.0 87 10 10% 1%
## 2 10 128 4602.0 85 4 10% 0%
## 3 8 130 4563.5 31 8 5% 1%
## 4 4 75 5626.0 42 6 7% 1%
## 5 11 168 3885.0 50 7 9% 1%
## 6 0 0 6928.0 27 8 4% 1%
## 7 7 132 4528.0 27 9 5% 2%
## 8 0 0 6928.0 15 4 3% 1%
## 9 9 81 5507.0 10 3 1% 0%
## 10 0 0 6844.0 29 1 4% 0%
## HouseRank Shape_Leng Shape_Area geometry
## 1 3248.0 11801.872 4388802.12 MULTIPOLYGON (((383285.3 80...
## 2 3486.0 2900.406 221746.84 MULTIPOLYGON (((383527.9 80...
## 3 5342.0 3468.762 270194.75 MULTIPOLYGON (((383473 8012...
## 4 4394.5 1647.461 96254.26 MULTIPOLYGON (((383976.7 80...
## 5 3736.0 3026.111 180076.58 MULTIPOLYGON (((384339 8012...
## 6 5924.0 4300.089 400488.04 MULTIPOLYGON (((384737 8013...
## 7 4815.0 22464.858 13996149.90 MULTIPOLYGON (((379360.2 80...
## 8 6394.0 4077.120 300877.30 MULTIPOLYGON (((386702 8018...
## 9 6868.0 8064.497 1877298.26 MULTIPOLYGON (((385842.5 80...
## 10 6174.5 15374.998 5542698.36 MULTIPOLYGON (((386014 8033...
Maybe we just want to find the right LA
unique(simd_shapes$LAName)
## [1] "Aberdeen City" "Aberdeenshire" "Angus"
## [4] "Argyll and Bute" "Clackmannanshire" "Dumfries and Galloway"
## [7] "Dundee City" "East Ayrshire" "East Dunbartonshire"
## [10] "East Lothian" "East Renfrewshire" "City of Edinburgh"
## [13] "Na h-Eileanan an Iar" "Falkirk" "Fife"
## [16] "Glasgow City" "Highland" "Inverclyde"
## [19] "Midlothian" "Moray" "North Ayrshire"
## [22] "North Lanarkshire" "Orkney Islands" "Perth and Kinross"
## [25] "Renfrewshire" "Scottish Borders" "Shetland Islands"
## [28] "South Ayrshire" "South Lanarkshire" "Stirling"
## [31] "West Dunbartonshire" "West Lothian"
simd_pc <- simd_shapes %>%
filter(LAName == "Perth and Kinross")
simd_pc
## Simple feature collection with 186 features and 51 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 233337.5 ymin: 694491.9 xmax: 335270.4 ymax: 785825.5
## Projected CRS: OSGB 1936 / British National Grid
## First 10 features:
## DataZone DZName LAName SAPE2017
## 1 S01011833 Powmill, Cleish and Scotlandwell - 01 Perth and Kinross 1014
## 2 S01011834 Powmill, Cleish and Scotlandwell - 02 Perth and Kinross 577
## 3 S01011835 Powmill, Cleish and Scotlandwell - 03 Perth and Kinross 926
## 4 S01011836 Powmill, Cleish and Scotlandwell - 04 Perth and Kinross 851
## 5 S01011837 Powmill, Cleish and Scotlandwell - 05 Perth and Kinross 712
## 6 S01011838 Kinross - 01 Perth and Kinross 893
## 7 S01011839 Kinross - 02 Perth and Kinross 974
## 8 S01011840 Kinross - 03 Perth and Kinross 1114
## 9 S01011841 Kinross - 04 Perth and Kinross 990
## 10 S01011842 Kinross - 05 Perth and Kinross 935
## WAPE2017 Rankv2 Quintilev2 Decilev2 Vigintilv2 Percentv2 IncRate IncNumDep
## 1 622 4853 4 7 14 70 5% 49
## 2 345 5883 5 9 17 85 4% 21
## 3 604 5011 4 8 15 72 5% 49
## 4 494 5232 4 8 15 75 4% 32
## 5 462 5627 5 9 17 81 2% 15
## 6 504 6940 5 10 20 100 2% 16
## 7 540 6790 5 10 20 98 2% 18
## 8 666 5800 5 9 17 84 6% 63
## 9 578 4413 4 7 13 64 8% 82
## 10 555 3512 3 6 11 51 11% 104
## IncRankv2 EmpRate EmpNumDep EmpRank HlthCIF HlthAlcSR HlthDrugSR HlthSMR
## 1 5074.0 4% 26 5032.0 55 24 17 102
## 2 5642.0 3% 10 5862.0 50 7 60 51
## 3 4891.0 3% 19 5710.0 50 20 0 53
## 4 5574.0 4% 18 5370.0 40 4 23 66
## 5 6434.0 3% 12 6105.0 30 39 24 72
## 6 6566.0 2% 11 6390.0 30 20 21 56
## 7 6546.0 3% 14 6082.5 40 7 35 61
## 8 4741.0 4% 24 5411.0 60 14 61 51
## 9 3840.5 6% 37 3935.0 95 58 117 71
## 10 3133.0 10% 53 2826.0 95 84 287 84
## HlthDprsPc HlthLBWTPc HlthEmergS HlthRank EduAttend EduAttain EduNoQuals
## 1 15% 0% 73 5300 88% 5.82 34
## 2 9% 5% 63 6336 92% 5.86 42
## 3 16% 6% 65 5863 81% 6.08 30
## 4 14% 0% 71 6252 95% 5.96 44
## 5 10% 0% 53 6697 90% 6.00 53
## 6 14% 0% 63 6557 94% 6.27 31
## 7 14% 9% 64 6224 89% 5.95 42
## 8 16% 7% 81 5083 86% 5.72 70
## 9 17% 11% 100 3392 82% 5.79 70
## 10 20% 6% 117 2729 89% 5.76 129
## EduPartici EduUniver EduRank GAccPetrol GAccDTGP GAccDTPost GAccDTPsch
## 1 0% 17% 6013 5.24 9.48 4.19 3.49
## 2 1% 17% 6050 4.25 4.17 3.99 4.23
## 3 3% 14% 5532 4.03 5.62 5.46 5.04
## 4 1% 22% 6490 2.63 7.68 6.79 2.84
## 5 1% 17% 5796 3.92 6.19 5.91 5.21
## 6 2% 20% 6615 2.92 1.19 2.34 3.14
## 7 3% 11% 5472 2.93 2.09 3.05 3.29
## 8 2% 11% 4552 2.11 2.03 2.27 2.35
## 9 2% 12% 4369 3.80 3.72 1.65 2.95
## 10 1% 16% 4436 2.57 2.71 1.26 1.84
## GAccDTSsch GAccDTRet GAccPTGP GAccPTPost GAccPTRet GAccBrdbnd GAccRank
## 1 11.26 8.58 20.51 12.41 18.51 26% 485
## 2 5.36 4.07 10.62 9.60 9.87 56% 1221
## 3 9.89 7.70 14.80 14.88 19.12 61% 512
## 4 9.95 10.01 19.80 16.50 26.19 23% 502
## 5 8.77 8.82 23.34 19.67 32.71 33% 389
## 6 2.67 2.51 4.57 6.10 6.67 8% 5858
## 7 3.52 3.07 5.35 9.57 10.53 0% 4884
## 8 3.68 2.29 7.78 9.16 9.24 0% 5537
## 9 5.16 1.68 10.71 5.78 5.86 1% 4607
## 10 4.09 1.28 8.20 4.58 4.64 1% 6255
## CrimeCount CrimeRate CrimeRank HouseNumOC HouseNumNC HouseOCrat HouseNCrat
## 1 5 51 6150 18 28 2% 3%
## 2 0 0 6453 3 3 1% 1%
## 3 5 56 6058 31 10 5% 1%
## 4 9 109 4955 22 8 3% 1%
## 5 0 0 6808 7 9 1% 1%
## 6 9 104 5039 20 8 2% 1%
## 7 3 32 6531 40 1 4% 0%
## 8 4 37 6432 50 8 5% 1%
## 9 13 135 4458 65 6 7% 1%
## 10 20 209 3267 131 7 14% 1%
## HouseRank Shape_Leng Shape_Area geometry
## 1 5868 33413.287 24102701.3 MULTIPOLYGON (((302460.5 70...
## 2 6890 34190.960 16499300.0 MULTIPOLYGON (((311407.8 70...
## 3 5379 42221.932 28189611.8 MULTIPOLYGON (((314131.4 69...
## 4 6163 45152.024 17637636.0 MULTIPOLYGON (((316108.8 70...
## 5 6626 47123.950 28699147.7 MULTIPOLYGON (((313323.5 70...
## 6 6339 6301.371 379230.7 MULTIPOLYGON (((311472.4 70...
## 7 6065 3779.878 229797.3 MULTIPOLYGON (((311321 7034...
## 8 5468 7100.581 596025.5 MULTIPOLYGON (((311166 7031...
## 9 4726 5175.039 445845.7 MULTIPOLYGON (((311459.2 70...
## 10 2353 4032.762 263301.4 MULTIPOLYGON (((311398 7026...
# The bounding box of the filtered and unfiltered selection
st_bbox(simd_pc)
## xmin ymin xmax ymax
## 233337.5 694491.9 335270.4 785825.5
## xmin ymin xmax ymax
## 5513.0 530252.8 470323.0 1220301.5
# So, the bounding box is narrowed when the selection is narrowed
## tmap mode set to plotting
tm_shape(simd_pc) +
tm_polygons(col = "HlthSMR")
Combining
Let’s now try to combine both datasets
## tmap mode set to plotting
tm_shape(simd_pc) +
tm_polygons(col = "HlthSMR") +
tm_shape(defib_points) +
tm_dots(col = "red")
Or interactive…
## tmap mode set to interactive viewing
tm_basemap(leaflet::providers$OpenStreetMap.Mapnik) +
tm_shape(simd_pc) +
tm_polygons(col = "HlthSMR", alpha = 0.3) + # set transparency
tm_shape(defib_points) +
tm_dots(col = "red")
Conclusion/Discussion
- Thanks to
sf, spatial data much easier
and more compatible with tidyverse than it used to be.
- Easy to hook into leaflet through tmap view mode to get interactive
charts
- Relatively quick straightfoward to combine layers to explore
potential associations etc.
Not covered
- Spatial statistics and operations (e.g. finding perimeters around
points etc)
- Cartograms (very cool!)
- More efficient ways of querying data online through APIs instead of
downloading first
- More careful graphical refinements and ways of improving the
aesthetics of maps for different purposes.